Complete the code to create a simple neural network layer using the correct activation function.
layer_output = activation([1](inputs))The sigmoid activation function is often used in simple neural networks for binary outputs. Here, it is the correct choice for the activation function.
Complete the code to define the batch size for training a model.
model.fit(data, labels, batch_size=[1])A batch size of 32 is a common choice that balances memory use and training speed, helping scalability.
Fix the error in the code to correctly initialize a scalable model architecture.
model = Sequential() model.add(Dense(64, activation=[1]))
The activation parameter expects a string name of the activation function, so it must be in quotes.
Fill both blanks to create a dictionary comprehension that maps layer names to their output sizes only if the size is greater than 100.
layer_sizes = {layer[1]: size for layer, size in layers.items() if size [2] 100}The layer names are converted to uppercase with .upper(), and only layers with size greater than 100 are included.
Fill all three blanks to create a dictionary that maps model names in uppercase to their accuracy scores only if accuracy is above 0.8.
accuracies = [1]{name[2]: score for name, score in results.items() if score [3] 0.8}
The dictionary is created with dict(, model names are converted to uppercase with upper(), and only accuracies greater than 0.8 are included.